W SABIE: Scaling Up To Large Vocabulary Image Annotation
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Abstract Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at the top of the ranked list of annotations for a given image and learning a lowdimensional joint embedding space for both images and annotations. Our method, called W SABIE, both outperforms several baseline methods and is faster and consumes less memory.
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